论文标题
迪斯科：具有本地隐式功能的对抗性防御
DISCO: Adversarial Defense with Local Implicit Functions
论文作者
论文摘要
考虑到针对图像分类的对抗防御的问题，该问题是考虑到针对对抗性示例的分类器的鲁棒性。灵感来自于这些示例超出自然图像歧管的假设的启发，提出了一种具有局部隐式功能（迪斯科）的对抗性防御，以通过局部歧管预测来消除对抗性扰动。迪斯科消耗对抗图像和查询像素位置，并在该位置输出干净的RGB值。它是用编码器和局部隐式模块实现的，前者在该模块中产生人均深度功能，后者使用查询像素附近的功能来预测清洁的RGB值。广泛的实验表明，迪斯科及其级联版本的表现都优于先前的防御，而不管攻击者是否知道防守。迪斯科还显示出数据和参数效率，并安装跨数据集，分类器和攻击传输的防御措施。
The problem of adversarial defenses for image classification, where the goal is to robustify a classifier against adversarial examples, is considered. Inspired by the hypothesis that these examples lie beyond the natural image manifold, a novel aDversarIal defenSe with local impliCit functiOns (DISCO) is proposed to remove adversarial perturbations by localized manifold projections. DISCO consumes an adversarial image and a query pixel location and outputs a clean RGB value at the location. It is implemented with an encoder and a local implicit module, where the former produces per-pixel deep features and the latter uses the features in the neighborhood of query pixel for predicting the clean RGB value. Extensive experiments demonstrate that both DISCO and its cascade version outperform prior defenses, regardless of whether the defense is known to the attacker. DISCO is also shown to be data and parameter efficient and to mount defenses that transfers across datasets, classifiers and attacks.
